Sampling Framework

Sampling frameworks encompass methods for efficiently drawing representative samples from complex, high-dimensional probability distributions, crucial for various applications including statistical inference, machine learning, and recommendation systems. Current research emphasizes developing novel algorithms, such as actor-critic methods and score-based generative models, to address challenges posed by high dimensionality, data sparsity, and non-convexity in the target distributions. These advancements improve the accuracy and efficiency of sampling, leading to better model training, more robust hypothesis testing, and enhanced performance in applications like personalized recommendations and generative modeling.